Meng Yingchao, Zhang Zhongping, Yin Huaqiang, Ma Tao
Institute of Nuclear and New Energy Technology, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Tsinghua University, Beijing 100084, China.
Department of Electrical and Computer Engineering, University of Rochester, Rochester, New York 14642, USA.
Micron. 2018 Mar;106:34-41. doi: 10.1016/j.micron.2017.12.002. Epub 2017 Dec 21.
To obtain size distribution of nanoparticles, scanning electron microscope (SEM) and transmission electron microscopy (TEM) have been widely adopted, but manual measurement of statistical size distributions from the SEM or TEM images is time-consuming and labor-intensive. Therefore, automatic detection methods are desirable. This paper proposes an automatic image processing algorithm which is mainly based on local adaptive Canny edge detection and modified circular Hough transform. The proposed algorithm can utilize the local thresholds to detect particles from the images with different degrees of complexity. Compared with the results produced by applying global thresholds, our algorithm performs much better. The robustness and reliability of this method have been verified by comparing its results with manual measurement, and an excellent agreement has been found. The proposed method can accurately recognize the particles with high efficiency.
为了获得纳米颗粒的尺寸分布,扫描电子显微镜(SEM)和透射电子显微镜(TEM)已被广泛采用,但通过SEM或TEM图像手动测量统计尺寸分布既耗时又费力。因此,自动检测方法是很有必要的。本文提出了一种主要基于局部自适应Canny边缘检测和改进的圆形霍夫变换的自动图像处理算法。该算法可以利用局部阈值从具有不同复杂程度的图像中检测颗粒。与应用全局阈值产生的结果相比,我们的算法表现得更好。通过将该方法的结果与手动测量结果进行比较,验证了该方法的稳健性和可靠性,并且发现两者具有很好的一致性。所提出的方法能够高效准确地识别颗粒。